A variety of data mining methods exist for automatically detecting behavioral relationships between items stored or represented in a data repository. For example, in the context of an electronic catalog of items, data mining processes are commonly used to identify items that tend to be viewed, purchased, downloaded, or otherwise selected in combination. Different types of item relationships may be detected based on different types of user activity. For instance, a pair of items, A and B, may be identified as likely substitutes if a relatively large number of those who view A also view B during the same browsing session. Items C and D, on the other hand, may be identified as likely being complementary of each other if a relatively large number of those who purchase C also purchase D.
The item relationships detected using such processes may be exposed to users to assist users in identifying and selecting items of interest. For example, in some electronic catalog systems, when a user views an item, the user is informed of other items that are commonly viewed or bought by those who have viewed or purchased this item. Although this type of data assists the user in identifying a set of candidate items from which to select (e.g., a set of consumer electronics products with similar features), it generally does not help the user discriminate between these candidate items. Thus, the user typically must rely solely on the descriptions of the candidate items, including any user ratings and reviews, in making a purchase decision.
The need for users to more effectively identify and select between alternative items also arises in a variety of other contexts. For example, in a social networking system, a user may wish to identify a set of users that satisfy particular criteria, and to then select a particular user with which to establish a connection or a communication session. As another example, a user may wish to identify RSS (Really Simple Syndication) or news feeds related to a particular topic, and to then select a particular feed to which to subscribe.
A need also exists in the data mining field to more effectively identify items that are useful in combination. Currently, some e-commerce web sites use purchase-based item relationships (“customers who bought A also bought B”) to automatically select pairs of items to suggest purchasing in combination. In some cases, however, this process results in poor bundling suggestions.